What data for this category looks like
This category of data includes documents such as inspection specifications, sampling plans, incoming material reports, supplier audit reports, exception contact sheets, 8D reports, and concession acceptance approvals. Data sources include purchase orders, drawing specifications, supplier quality certificates, inspection equipment data, historical quality records, and supplier portal information. Data updates are triggered with each batch of incoming materials, monthly or quarterly supplier evaluations, and ad-hoc audits. Fields include batch number, supplier code, inspection items, defect types, rectification status, and additional relevant fields. This setup must adapt to scenarios where multi-source heterogeneous business documents and structured data are stored together.
What constraints do these characteristics impose on database and operations workflows
Multi-source heterogeneous business document structures require databases to support flexible field expansion. Fixed-schema storage with strict constraints is not recommended. High-frequency incoming material data updates require adaptation to high-concurrency write scenarios to avoid single-table write bottlenecks. Data association requirements across suppliers, batches, and rectification processes require databases to support complex associated queries. Long data retention periods and quality traceability-related needs necessitate a reliable incremental backup configuration. Additionally, data update frequencies vary significantly across different business scenarios, requiring dynamic adjustment of data sharding strategies to balance read and write performance.
How to set configurations
This document applies to FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://admin:securepass@mongo.example.com:27017/fastgpt_iqc?replicaSet=rs0 | Adapts to multi-source heterogeneous incoming material inspection documents and historical quality data storage, supports flexible field expansion |
PG_URL | postgresql://admin:securepass@pg.example.com:5432/fastgpt_sqe | Stores structured supplier audit reports, 8D reports, and concession acceptance approval data, supports complex associated queries |
DB_MAX_LINK | 80–120 connections | Adapts to the concurrency requirements of high-frequency writes for each batch of incoming materials, prevents database connection exhaustion |
REDIS_URL | redis://:securepass@redis.example.com:6379/1 | Caches frequently queried sampling plans and supplier qualification data, reduces repeated database calls |
Three common misconfigurations
- Symptom: An error
mongo version incompatibleoccurs after starting the service, and the log shows missing AVX instruction set support. Cause: The database image version was not adjusted based on server hardware conditions, and the officially recommended high-version MongoDB image was used directly, resulting in compatibility issues. - Symptom: System response latency is high when repeatedly executing the same incoming material inspection query. Cause: The
REDIS_URLparameter was not configured to enable caching, so each query directly accesses the database without reusing historical query results. - Symptom: The storage location of exception contact sheets cannot be located, and the query returns empty results. Cause: The storage separation strategy for structured and unstructured data was not clearly defined, and the usage scenarios of
PG_URLandMONGODB_URIwere confused, leading to incorrect data query paths.
How to verify successful configuration
- Run the
mongo --versioncommand to check the connected MongoDB version, confirm compatibility with server hardware, and avoid errors related to AVX instruction sets. - Run the
redis-cli pingcommand to verify that the Redis connection is normal, and confirm that the cache configuration is active. - Submit a batch of simulated incoming material inspection data, and query the corresponding structured and unstructured data to confirm that data can be written and read normally.
- Check the database connection number monitoring panel, confirm that the current connection number does not exceed the threshold set by the
DB_MAX_LINKconfiguration.
The values provided are common starting points and should be evaluated against the specific samples of the target deployed environment.